base_model: Qwen/Qwen-Image
library_name: diffusers
license: apache-2.0
pipeline_tag: text-to-image
tags:
- qwen-image
- text-to-image
- quantized
- fp8
- e4m3
- diffusers
base_model_relation: quantized
Qwen-Image-e4m3
FP8 (e4m3) dynamically-quantized Qwen-Image,
saved as a complete QwenImagePipeline.
What was changed
All 60 blocks of the QwenImageTransformer2DModel are quantized to
e4m3_e4m3_dynamic — float8_e4m3fn weights with dynamically scaled
float8_e4m3fn activations. The Qwen2.5-VL text encoder, the VAE and the
transformer's non-block tensors are untouched and stay in bf16. The transformer
drops from ~40.9 GB to ~20.5 GB.
Quantization was done with WaveSpeed's xelerate.ao.quantize. Weights are
stored as pickled .bin shards, so loading requires use_safetensors=False.
FP8 matmul needs Hopper (H100/H200) or newer to be faster than bf16; on older GPUs the weights are dequantized on the fly and you only get the memory saving.
One caveat worth repeating from our own testing: on Qwen-Image, running fp8 weight-and-activation matmul under a fully fused fast path produces visible quality loss. The configuration published here — dynamic per-tensor activation scaling with bf16 accumulation — is the one that holds up.
Usage
import torch
from diffusers import QwenImagePipeline
pipe = QwenImagePipeline.from_pretrained(
"wavespeed/Qwen-Image-e4m3",
torch_dtype=torch.bfloat16,
use_safetensors=False,
).to("cuda")
image = pipe("a chalkboard menu written in neat cursive").images[0]
License
Apache-2.0, inherited from Qwen-Image.